Asset Intelligence system provides a comprehensive diagnosis of the transformer's health status, explaining the possible causes of the fault and listing the next steps for maintenance or repair.

The system's diagnostic accuracy is enhanced by considering the correlation between different monitoring parameters, which is often neglected in traditional fault detection methods. Our proposed system can be used by power system operators, maintenance engineers, and other stakeholders involved in the maintenance and operation of transformers.

It provides an effective solution for early detection of transformer faults, which can reduce the risk of unexpected outages, save maintenance costs, and improve the overall reliability of power systems.

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